Floating raft aquaculture is an important part of mariculture, and the use of single source data is easy to inhibits the extraction of floating raft aquaculture, so the advantages of integrating multisource data are particularly important. However, multisource remote sensing fusion still has the problem of data information imbalance and feature redundancy. In this paper, a channel exchanging bottleneck attention network (CEBANet) is proposed to extract features from multisource remote sensing data, determine whether features are redundant by using the scaling factor of batch normalization (BN) layer, replace the current redundant features with another modal feature. Bottleneck attention module (BAM) and feature calibration module are added to improve the capability of feature extraction and receptive field processing. The CEBANet is optimized by the sparse constraint on channel exchanging condition and cross-entropy loss and consistency loss. Using GF-5 and GF-3 data from Jinzhou District of Dalian City, it is proved that the proposed model can realize the complementary advantages of multisource remote sensing data and improve the accuracy of extracting floating raft aquaculture areas.
In this paper, the 5-instant discretization formula (5IDF), renowned for its high computational accuracy in approximating the first-order derivative, is employed for the discretization of the continuous-time zeroing neural network (CTZNN). The formula is then compared with other common difference formulas of varying precision. Based on this formulation, the 5IDF-type discrete-time zeroing neural network (DTZNN) model is introduced and studied for solving discrete-form time-varying matrix inversion. The feasibility, effectiveness, and superiority of the proposed 5IDF-type DTZNN model for addressing discrete-form time-varying matrix inversion are further substantiated through numerical experiments.
Idiopathic pulmonary fibrosis (IPF) is a chronic lung disease that significantly impacts individuals' health. Early diagnosis of IPF can enhance patient survival rates. Considering the limited research on automatic detection of IPF in academic circles and the scarcity of labeled data, this paper proposes a transfer learning-based algorithm for detecting IPF lesion areas. The proposed method consists of two stages: firstly, construct an IPF-like dataset comprising natural texture images with similar visual features to IPF, and pre-train the U-net network using this dataset; secondly, fine-tune the network using both the pre-trained weights and actual IPF data, followed by utilizing the trained network for lesion area detection. Experimental results demonstrate substantial improvements in terms of accuracy and sensitivity compared to corner distribution-based methods for IPF detection.
The frequent occurrence of emergencies in recent years has seriously affected the stability and development of society and economy. Effectively responding to emergencies has become a crucial research topic. A large number of emergency cases contain potential rules and valuable response experience in the process of occurrence, development and response, and the association rule mining can discover the fine-grained and valuable "if scenario then response" association rules in the cases. The knowledge of emergency response rules obtained from mining can assist the generation of response programs under actual emergency scenarios and provide reference basis for emergency decision-making. The existing methods only consider the support and confidence indicators but ignore the lift and comprehensibility of the rules and the lack of domain knowledge guidance in the mining process. Aiming at the problem, this paper proposes a response rule mining method based on knowledge guidance and improved genetic algorithm, which effectively reduces the number of low-quality rules and attributes of the mined rules and improves the quality and efficiency of association rule mining.
On the basis of the gradient-based differential k-winners-take-all (GD-kWTA) network, this paper focuses on its applications on multi-robot competition and coordination under noise-free condition. The GD-kWTA network offers improved handling of temporal lag in control variables under noise-free condition. At the same time, theorems and relevant validations are presented to ensure the exponential convergence and antinoise elasticity of the proposed GD-kWTA network. Finally, the GD-kWTA network is integrated with a consensus filter as a robust control strategy for the simulation of the coordinated competitive behavior of various robots. Even under noise-free condition and when handling complex target trajectory curves, the GD-kWTA network successfully achieves satisfactory results, which underscores its effectiveness in tackling the issue of temporal lag.
